Collaborative Research: Selecting Sensors and Actuators for Topologically Evolving Networked Dynamical Systems: Battling Contamination in Water Networks

合作研究：为拓扑演化的网络动力系统选择传感器和执行器：对抗水网络中的污染

基本信息

批准号：
1728605
负责人：
Tyler Summers
金额：
$ 15万
依托单位：
University of Texas at Dallas
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-01 至 2021-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1728605&HistoricalAwards=false
关键词：
Collaborative Research Selecting Sensors Actuators

项目摘要

The objective of this project is the dynamic management of sensors and actuators in networked systems with applications to minimizing contamination in drinking water networks. A defining feature of modern infrastructures is the prevalence and abundance of real-time sensing and actuation devices. The increased integration of smart communities through Internet-enabled devices will achieve superior system-level infrastructure performance and reliability. Power grids, water systems, and transportation networks share billions of sensors and actuators amongst them. Although the exponential increase in the number of sensing and actuating devices offers an abundance of societal merits, the real-time management of these devices becomes a daunting task for system stakeholders. The dynamic deployment of sensors and actuators, actuators implementing optimal actions, and sensors selectively reporting time- and space-critical data, will lead to significant socio-economic gains. The specific themes addressed by this project are curbing contamination levels in water distribution networks and reducing energy consumption in power grids. The research goal of this project is to create fundamental scientific methods that guide networked systems stakeholders in the adaptive selection of the most reliable sensors and actuators--amid the inevitable topologically evolution and uncertainty in these systems. The low- or high-frequency topological evolution is a natural consequence of the physical changes in networked systems. For example, the addition of nodes and links in networks causes a change in topology. Related prior work focused on problems of scheduling or one-time placement of sensors and actuators for mostly linear systems. In contrast, this research investigates methods for adaptively selecting sensing and actuating devices as network conditions change, in addition to considering a wide range of control-theoretic metrics. Such an approach significantly enhances the resilience of networked systems and infrastructures to changes in topology, in addition to ensuring robustness against uncertainty. The investigated research also has important impacts on quality control of contamination-free water distribution networks that leverage high-end mobile water sensors traversing pipes and tanks, while acquiring data through wireless communications every few seconds. Exploiting the slow time-scales of water networks, optimal and sub-optimal online algorithms are developed based on semidefinite and mixed-integer programming. These algorithms capitalize on the inherent sparsity of networked systems to obtain the optimal timing and location of decontaminant injections, acting as actuators, while simultaneously sampling data from mobile water sensors. This can ultimately guarantee a minimal level of contamination in drink water networks.

该项目的目标是对网络系统中的传感器和执行器进行动态管理，并应用最大限度地减少饮用水网络中的污染。现代基础设施的一个决定性特征是实时传感和驱动设备的普及和丰富。通过支持互联网的设备加强智能社区的集成将实现卓越的系统级基础设施性能和可靠性。电网、供水系统和交通网络共享数十亿个传感器和执行器。尽管传感和驱动设备数量的指数增长带来了丰富的社会效益，但这些设备的实时管理成为系统利益相关者的一项艰巨任务。传感器和执行器的动态部署、执行最佳动作的执行器以及有选择地报告时间和空间关键数据的传感器将带来显着的社会经济收益。该项目的具体主题是控制供水网络的污染水平和减少电网的能源消耗。该项目的研究目标是创建基本的科学方法，指导网络系统利益相关者在这些系统不可避免的拓扑演化和不确定性中自适应选择最可靠的传感器和执行器。低频或高频拓扑演化是网络系统中物理变化的自然结果。例如，网络中节点和链路的添加会导致拓扑发生变化。相关的先前工作主要集中于大多数线性系统的传感器和执行器的调度或一次性放置问题。相比之下，除了考虑广泛的控制理论指标之外，本研究还研究了随着网络条件变化自适应选择传感和驱动设备的方法。除了确保针对不确定性的鲁棒性之外，这种方法还显着增强了网络系统和基础设施对拓扑变化的适应能力。所调查的研究还对无污染配水网络的质量控制产生了重要影响，该网络利用穿过管道和水箱的高端移动水传感器，同时通过无线通信每隔几秒钟获取数据。利用水网络的慢时间尺度，基于半定和混合整数规划开发了最优和次优在线算法。这些算法利用网络系统固有的稀疏性来获得净化剂注入的最佳时间和位置，充当执行器，同时从移动水传感器采样数据。这最终可以保证饮用水网络中的污染达到最低水平。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Simultaneous Sensor and Actuator Selection/Placement through Output Feedback Control

通过输出反馈控制同时选择/放置传感器和执行器

DOI：
10.23919/acc.2018.8431548
发表时间：
2018-01
期刊：
Proceedings of the American Control Conference
影响因子：
0
作者：
Nugroho, Sebastian;Taha, Ahmad;Summers, Tyler;Gatsis, Nikolaos
通讯作者：
Gatsis, Nikolaos

Sparse optimal control of networks with multiplicative noise via policy gradient

通过策略梯度对具有乘性噪声的网络进行稀疏最优控制

DOI：
10.1016/j.ifacol.2019.12.176
发表时间：
2019-01
期刊：
IFAC-PapersOnLine
影响因子：
0
作者：
Gravell, Benjamin;Guo, Yi;Summers, Tyler
通讯作者：
Summers, Tyler

Time-Varying Sensor and Actuator Selection for Uncertain Cyber-Physical Systems

不确定网络物理系统的时变传感器和执行器选择

DOI：
10.1109/tcns.2018.2873229
发表时间：
2017-08-26
期刊：
IEEE Transactions on Control of Network Systems
影响因子：
4.2
作者：
A. Taha;Nikolaos Gatsis;T. Summers;Sebastian A. Nugroho
通讯作者：
Sebastian A. Nugroho

Distributionally Robust Stochastic Optimal Water Flow and Risk Management

分布鲁棒随机最优水流和风险管理

DOI：
10.1016/j.ifacol.2020.12.790
发表时间：
2024-09-13
期刊：
IFAC-PapersOnLine
影响因子：
0
作者：
Yi Guo;T. Summers
通讯作者：
T. Summers

Sensor Selection for Dynamics-Driven User-Interface Design

动力学驱动的用户界面设计的传感器选择

DOI：
10.1109/tcst.2021.3056242
发表时间：
2021-02
期刊：
IEEE Transactions on Control Systems Technology
影响因子：
4.8
作者：
Vinod, Abraham P.;Thorpe, Adam J.;Olaniyi, Philip A.;Summers, Tyler H.;Oishi, Meeko M.
通讯作者：
Oishi, Meeko M.